Method and apparatus for providing privacy policy generation based on in-game behavior data

ABSTRACT

An approach is provided of generating real-world privacy policies based on in-game behavior. A privacy policy platform determines in-game behavior data associated with at least one user while the at least one user is playing at least one location-based game. The privacy platform causes, at least in part, a mapping of the in-game behavior data to one or more game locations within the at least one location-based game. The privacy platform further causes, at least in part, a correlation of the one or more game locations to one or more real-world locations. The privacy platform then causes, at least in part, a generating of one or more privacy policies for the one or more real-world locations based, at least in part, on the in-game behavior data mapped to the correlated one or more game locations.

BACKGROUND

Recent advances in mapping and gaming technologies have led tocompelling multi-player location-based games that model real-worldenvironments. Players of such location-based games have been found toexhibit a connection with in-game locations with which they have anexisting relationship in real-life (e.g., home, office, favoriterestaurants, and/or other points of interest).

At the same time, within an increasingly connected communicationsenvironment, players and users in general are increasingly concernedwith ensuring the privacy of their data and/or other interactions withtheir network devices and services. In particular, many users areconcerned with the issue of location-based privacy (e.g., how todetermine the users/devices with whom a user's device is allowed toconnect, share with, etc.) and how to generate location-based privacypolicies as little burden as possible on the users.

Accordingly, service providers and device manufacturers face significanttechnical challenges to facilitating automated location-based privacypolicy generation using existing data already available about users(e.g., in-game behavior data collected from location-based games).

SOME EXAMPLE EMBODIMENTS

Therefore, there is a need for an approach for providing privacy policygeneration based on in-game behavior data.

According to one embodiment, a method comprises determining in-gamebehavior data associated with at least one user while the at least oneuser is playing at least one location-based game. The method alsocomprises causing, at least in part, a mapping of the in-game behaviordata to one or more game locations within the at least onelocation-based game. The method further comprises causing, at least inpart, a correlation of the one or more game locations to one or morereal-world locations. The method further comprises causing, at least inpart, a generating of one or more privacy policies for the one or morereal-world locations based, at least in part, on the in-game behaviordata mapped to the correlated one or more game locations.

According to another embodiment, an apparatus comprises at least oneprocessor, and at least one memory including computer program code forone or more computer programs, the at least one memory and the computerprogram code configured to, with the at least one processor, cause, atleast in part, the apparatus to determine in-game behavior dataassociated with at least one user while the at least one user is playingat least one location-based game. The apparatus also causes, at least inpart, a mapping of the in-game behavior data to one or more gamelocations within the at least one location-based game. The apparatusfurther causes, at least in part, a correlation of the one or more gamelocations to one or more real-world locations. The apparatus furthercauses, at least in part, a generating of one or more privacy policiesfor the one or more real-world locations based, at least in part, on thein-game behavior data mapped to the correlated one or more gamelocations.

According to another embodiment, a computer-readable storage mediumcarries one or more sequences of one or more instructions which, whenexecuted by one or more processors, cause, at least in part, anapparatus to determine in-game behavior data associated with at leastone user while the at least one user is playing at least onelocation-based game. The apparatus also causes, at least in part, amapping of the in-game behavior data to one or more game locationswithin the at least one location-based game. The apparatus furthercauses, at least in part, a correlation of the one or more gamelocations to one or more real-world locations. The apparatus furthercauses, at least in part, a generating of one or more privacy policiesfor the one or more real-world locations based, at least in part, on thein-game behavior data mapped to the correlated one or more gamelocations.

According to another embodiment, an apparatus comprises means fordetermining in-game behavior data associated with at least one userwhile the at least one user is playing at least one location-based game.The apparatus also comprises means for causing, at least in part, amapping of the in-game behavior data to one or more game locationswithin the at least one location-based game. The apparatus furthercomprises means for causing, at least in part, a correlation of the oneor more game locations to one or more real-world locations. Theapparatus further comprises means for causing, at least in part, agenerating of one or more privacy policies for the one or morereal-world locations based, at least in part, on the in-game behaviordata mapped to the correlated one or more game locations.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing the method of any of the filedclaims.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1 is a diagram of a system capable of providing privacy policygeneration based on in-game behavior data, according to one embodiment;

FIG. 2 is a diagram of the components of a privacy platform/privacyplatform module, according to one embodiment;

FIG. 3 is a flowchart of a process for providing privacy policygeneration based on in-game behavior data, according to one embodiment;

FIG. 4 is a flowchart of a process for classifying in-game behavior togenerate privacy policies, according to one embodiment;

FIG. 5 is a flowchart of a process for determining sensitivity statusinformation for locations based on in-game behavior data, according toone embodiment;

FIGS. 6A-6C are user interface diagrams depicting a process forproviding privacy policy generation based on in-game behavior data,according to various example embodiments;

FIG. 7 is a diagram of hardware that can be used to implement anembodiment of the invention;

FIG. 8 is a diagram of a chip set that can be used to implement anembodiment of the invention; and

FIG. 9 is a diagram of a mobile terminal (e.g., handset) that can beused to implement an embodiment of the invention.

DESCRIPTION OF SOME EMBODIMENTS

Examples of a method, apparatus, and computer program for providingprivacy policy generation based on in-game behavior data are disclosed.In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It is apparent,however, to one skilled in the art that the embodiments of the inventionmay be practiced without these specific details or with an equivalentarrangement. In other instances, well-known structures and devices areshown in block diagram form in order to avoid unnecessarily obscuringthe embodiments of the invention.

Although the various embodiments discussed herein refer to generatingprivacy policies, it is contemplated that the approaches presented inthe embodiments are also applicable to any type of policy (e.g.,security policy, access policies, etc.) that can be applied to a userdevice. Moreover, although the policies described herein are discussedas location-based policies (e.g., polices associated with specificlocations such as in-game and/or real-world locations), it iscontemplated that the approaches presented in the embodiments are alsoapplicable to policies based on other contextual parameters (e.g., bycontact, by activity, by time, etc.).

FIG. 1 is a diagram of a system capable of providing privacy policygeneration based on in-game behavior data, according to one embodiment.Traditionally, the process for generating privacy policies can be verytime and/or effort consuming. For example, with respect tolocation-based privacy policies, traditional policy processes generallyconsist of a user manually setting privacy policies for each location,for each type of action, for each contact, etc., one by one. However,this is a potentially burdensome process which many users are unwillingto unwilling to undertake. As a result, users tend to avoid suchgenerating such policies, with the end result being that only simplified“All-or-Nothing” types of policies get used practice. Instances of such“All-or-Nothing” policies include settings in current mobile platformswhere location-based services are either enabled for an application ornot, without the ability to specify specific locations where thelocation-based services are enabled and where they are not.

To address this problem, a system 100 of FIG. 1A introduces a capabilityto automate the generation of location oriented policies by “learning”from user behavior data collected while playing location-based games. Inone embodiment, the system 100 tracks a user's behavior and actions atparticular in-game locations while playing a location-based game. By wayof example, location-based games include any type of game, application,and/or service that enables a user to interact with other players (ortheir virtual counterparts) at one or more in-game locations that can becorrelated to real-world locations. Based on this in-game behavior data,the system 100 generates privacy policies specific to real-worldlocations that correspond to the in-game locations.

In one embodiment, location-oriented privacy policies control, forinstance, what data users share with applications via their devices,which activities the devices perform, how the devices interact withother users, etc. In other words, these privacy policies can restrict orpermit access to various device functions such as: accessing one or moresensors (e.g., a camera sensor, a location sensor, a microphone, etc.);sharing location information at specific locations (e.g., locationsdetermined to be “sensitive” to a user); device pairing at specificlocations; etc. For example, one sample policy can be of the form: “Donot allow application A to turn on the device camera when the user U isin a sensitive location L.”

By way of example, sample use-cases for how the system 100 can transforma user's in-game behavior data (e.g., collected from a location-basedgame) to location-based privacy policies are as follows:

-   -   (1) User U spends time>t at location L in a game. This        observation is used to infer that in-game location L is a        “sensitive” location for user U, and a privacy policy is defined        to switch off all location sharing activities on user U′s device        whenever the user U or the device is at the real-world        equivalent of location L.    -   (2) User U always engages in activity X at location L in the        game. This observation is used to define a privacy policy that        allows sensors and/or data corresponding to activity X to be        shared, while restricting access to all other sensors and/or        data by applications on user U's device, whenever user U is at        the real-world equivalent of location L.    -   (3) User U avoids player P at location L in the game, but        interacts with player P at location L′. This observation is used        to define a privacy policy for user U that restricts user U's        device from pairing with player P's device at the real-world        equivalent of location L, but allows user U's device to pair        with player P′s device at the real-world equivalent of location        L′.

As shown in FIG. 1, in one embodiment, the system 100 includes userequipment (UE) 101 a (e.g., a mobile device) that can potentiallyinteract with any number of other user equipment 101 b-101 n. UEs 101 aand 101 b-101 n are also collectively referred to as UEs 101. In oneembodiment, the UEs 101 a-101 n may execute one or more gameapplications 103 a-103 n (also collectively referred to as gameapplications 103). By way of example, the game applications 103 mayinclude location-based games that enable players to interact within-game locations that can be correlated to or otherwise representreal-world locations. In some example game applications 103, the in-gamelocations may also be the actual real-world locations (e.g., inaugmented reality location-based games). In another embodiment, the gameapplications 103 enable players to interact with other players withinthe game environment. For example, a player playing the game application103 a on the UE 101 a may interact with other players playing gameapplications 103 a-103 n on the respective UEs 101 b-101 n. In oneembodiment, the game applications 103 can be configured to collect orotherwise generate game behavior data that represents user actionswithin the game environment as well as contextual information about theuser's gameplay (e.g., location, time, duration, etc.).

By way of example, the UE 101 is any type of mobile terminal, fixedterminal, or portable terminal including a mobile handset, station,unit, device, multimedia computer, multimedia tablet, Internet node,communicator, desktop computer, laptop computer, notebook computer,netbook computer, tablet computer, personal communication system (PCS)device, personal navigation device, personal digital assistants (PDAs),audio/video player, digital camera/camcorder, positioning device,television receiver, radio broadcast receiver, electronic book device,game device, or any combination thereof, including the accessories andperipherals of these devices, or any combination thereof. It is alsocontemplated that the UE 101 can support any type of interface to theuser (such as “wearable” circuitry, etc.).

In one embodiment, the UEs 101 a-101 n are configured with one or moresensors 105 a-105 n that can generate data used during gameplay, dataused for generating privacy policies, data that is subject to theprivacy policies of the UEs 101, or a combination thereof. By way ofexample, the sensors 105 may be any type of sensors. In certainembodiments, the sensors 105 may include, for example, a globalpositioning sensor for gathering location data, a network detectionsensor for detecting wireless signals or network data, a camera/imagingsensor for gathering image data, and the like. In one embodiment, thesensors 105 may further include light sensors, tilt sensors, pressuresensors, audio sensors (e.g., microphone), or receivers for differentshort-range communications (e.g., Bluetooth, WiFi, etc.). In anotherembodiment, the sensors 105 may determine the current device context andmay correlate the contextual information for application of privacypolicies appropriate for a given context.

In one embodiment, through the communication network 107, the UEs 101have connectivity to a privacy platform 109 to perform the functionsassociated with providing privacy policy generation based on in-gamebehavior data. In one embodiment, although the privacy platform 109 isdepicted in FIG. 1 as a network component, it is contemplated that theprivacy platform 109 may be resident within the UEs 101 as respectiveprivacy platform modules 110 a-110 n (also collectively referred to asprivacy platform modules 110) so that all or some of the functions ofthe privacy platform 103 may be performed locally at the UEs 101 by theprivacy platform modules 110. Accordingly, although various embodimentsare described with respect to the privacy platform 109 performing thefunctions and/or process for generating privacy policies based onin-game behavior data, the privacy platform modules 110 may also performsome or all of the same described function or processes.

In one embodiment, the user (and/or other parties such as a serviceprovider) may configure the system 100 to use either the networkcomponent (e.g., the privacy platform 109), the local component (e.g.,the privacy platform module 110), or the network and local components incombination to generate privacy policies. In one embodiment, theconfiguration of which component or components to use can be based on auser's overarching privacy settings. For example, a user's overarchingprivacy setting may specify that personal data should not be transmittedoutside of the user's device (e.g., the UE 101), the system 100 canconfigure the privacy platform module 110 to perform privacy policyfunctions locally at the device.

In one embodiment, the privacy platform 109, for instance, monitors auser's in-game behavior while the user is playing a location-based game103 on a UE 101 that reflects “real-life” or “real-world” locations. Theprivacy platform 109, for instance, performs a data mining of the user'sin-game behavior to extract a user profile with respect to features suchas locations visited, interaction with contacts, activities performed,etc. The privacy platform 109 then generates privacy policies based onthe features extracted from the in-game behavior data. For example, theprivacy platform 109 can feed the extracted features into policytemplates (e.g., stored in the database 111) to generate the privacypolicies.

In one embodiment, the privacy platform 109 can process the in-gamebehavior data to determine locations that are “sensitive” to the user.For example, the privacy platform 109 can use temporal criteria todetermine which in-game locations and/or real-world locations aresensitive. In one example use case, if the user stays or visits alocation greater than a threshold time duration, then the privacyplatform 109 can be configured to designate that location as sensitive.It is contemplated that the privacy platform 109 can use any criterionfor determining whether a location is sensitive including, for instance,a frequency of visits, user designation, crowd sourced information, etc.

In one embodiment, the in-game behavior may be requested directly fromthe location based game 103 (e.g., via an application programminginterface (API), library, etc.), or may interact with network componentsassociated with the game application 103. The in-game behavior data, forinstance, can be stored in database 111. Examples of these networkcomponents include a services platform 113, services 115 a-115 n (hereinafter services 115), and content providers 117 a-117 n (herein aftercontent providers 117).

In one embodiment, the services platform 113 may include any type ofservice associated with the game application 103 and/or in-game behaviordata associated with a user playing the game application 103. By way ofexample, the services platform 113 may include social networkingservices, content (e.g., audio, video, images, etc.) provisioningservices, application services, storage services, contextual informationdetermination services, location based services, information (e.g.,weather, news, etc.) based services, etc. In one embodiment, theservices platform 113 may interact with the UE 101, the privacy platform109 and the content providers 117 to supplement or aid in the processingof the content information. In another embodiment, the services platform113 may provide the privacy platform 109 with user preferenceinformation, contextual information etc., to assist the privacy platform109 in determining one or more privacy protecting actions for generatingprivacy policies based on in-game behavior data.

By way of example, services 115 may be an online service that reflectsinterests and/or activities of users. In one scenario, the services 115provide representations of each user (e.g., a profile), his/her sociallinks, and a variety of additional information. The services 115 allowusers to share activities information, contextual information, andinterests within their individual networks, and provides for dataportability. The services 115 may additionally assist in providing theprivacy platform 109 in determining sensitivity levels for one or moreinformation exchanged over the communication session. In one embodiment,the services 115 may further assist the privacy platform 109 in profilemapping to protect the privacy interest of users. In another embodiment,the accessibility of the information exchanged by one or more servicesmay be determined based, at least in part, on privacy policies generatedbased on in-game behavior data. Further, user privacy profiles may bespecific to each service, for example, services 115 may deduce privacypolicy settings based on user settings with similar or analogousservices using the same data.

The content providers 117 may provide content to the UE 101, the gameapplication 103, the privacy platform 109, and the services 115 of theservices platform 113. The content provided may be any type of content,such as textual content, audio content, video content, image content,etc. For example, the content providers 117 may provide content that maysupplement content of the game applications 103, the sensors 105, or acombination thereof. In another example, the content providers 117 mayprovide content that may aid the privacy platform 109 in generatingprivacy policies based on in-game behavior data such as providing policytemplates, recommended privacy settings, crowd-sourced privacy policies,etc. In one embodiment, the content providers 117 may also store contentassociated with the UE 101, the privacy platform 109, and the services115 of the services platform 113. In another embodiment, the contentproviders 117 may manage access to a central repository of data, andoffer a consistent, standard interface to user's data.

In one embodiment, the communication network 107 of system 100 includesone or more networks such as a data network, a wireless network, atelephony network, or any combination thereof. It is contemplated thatthe data network may be any local area network (LAN), metropolitan areanetwork (MAN), wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, or any other suitablepacket-switched network, such as a commercially owned, proprietarypacket-switched network, e.g., a proprietary cable or fiber-opticnetwork, and the like, or any combination thereof. In addition, thewireless network may be, for example, a cellular network and may employvarious technologies including enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., worldwide interoperability formicrowave access (WiMAX), Long Term Evolution (LTE) networks, codedivision multiple access (CDMA), wideband code division multiple access(WCDMA), wireless fidelity (WiFi), wireless LAN (WLAN), Bluetooth®,Internet Protocol (IP) data casting, satellite, mobile ad-hoc network(MANET), and the like, or any combination thereof.

In one embodiment, the privacy platform 109 may be a platform withmultiple interconnected components. The privacy platform 109 may includemultiple servers, intelligent networking devices, computing devices,components and corresponding software for generating privacy policiesbased on in-game behavior data.

By way of example, the UE 101, the privacy platform 109, the gameapplications 103, the services platform 113, and the content providers117 communicate with each other and other components of thecommunication network 107 using well known, new or still developingprotocols. In this context, a protocol includes a set of rules defininghow the network nodes within the communication network 107 interact witheach other based on information sent over the communication links. Theprotocols are effective at different layers of operation within eachnode, from generating and receiving physical signals of various types,to selecting a link for transferring those signals, to the format ofinformation indicated by those signals, to identifying which softwareapplication executing on a computer system sends or receives theinformation. The conceptually different layers of protocols forexchanging information over a network are described in the Open SystemsInterconnection (OSI) Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 2 is a diagram of the components of the privacy platform 109 and/orthe privacy platform module 110, according to one embodiment. By way ofexample, the privacy platform 109 and/or the privacy platform module 110include one or more components for providing privacy policy generationbased on in-game behavior data. It is contemplated that the functions ofthese components may be combined in one or more components or performedby other components of equivalent functionality. In this embodiment, theprivacy platform 109 and/or the privacy platform module 110 include abehavior module 201, a profiling module 203, a heuristics module 205,and a policy module 207. The modules 201-207 also have connectivity tothe database 111 for storing data associated with generating privacypolicies based on in-game behavior data.

In one embodiment, the behavior module 201 can monitor or otherwiseaccess user in-game behavior data generated while the user is playing alocation-based game (e.g., multi-player location-based games). Aspreviously discussed, location-based games can include applications,services, content, etc. that include as part of their game play in-gamelocations that are actual real-world locations or locations that can becorrelated to real-world locations. For example, a location-based gamemay include directing users to actual physical locations as part ofgameplay. In other examples, a location-based game may present a virtualrepresentation of real-world locations that a user can visit virtually.In yet other examples, a location-based game may present a mixedenvironment where an in-game location can be an analogous representationof a real-world environment.

In one embodiment, the behavior module 201 monitoring any type ofinteraction or event associated with the user within the location-basedgame as in-game behavior data. For example, the in-game behavior datamay include, but are not limited to, actions of interest in the gamesuch as: (a) locations bought or visited by the user in the game; (b)types of activities performed by the user at different locations; (c)places in the game where the user likes to spend time; and (d)interaction by the user with other players in the game at specificlocations and/or times within the game. By way of example, interactiondata include both players with whom the user interacts, as well as thosewhom the user “avoids”, at specific locations and/or times. In someembodiments, the interaction with other players is bilateral, in thatthe game action depends on choices made by both the user and the otherplayers in question.

In one embodiment, the behavior module 201 can monitor, retrieve, store,etc. in-game behavior data in the database 111. In this example, themonitored user actions or in-game behavior data are logged in thedatabase 111 hosted on the game terminal or device (e.g., UE 101) or acloud-based server (e.g., services platform 113 and/or services 115).

In one embodiment, after monitoring or logging the in-game behaviordata, the behavior module 201 interacts with the profiling module 203 tofurther process the in-game behavior data. In one embodiment, theprofiling module 203 performs data mining functions on themonitored/logged in-game behavior data to extract features forgenerating privacy policies. Examples of such features include, but arenot limited to: (1) locations l—including identifying the real-worldlocations that correlate or correspond to the in-game locations visitedby the user in the game; (2) contact c—including categorizing the gameplayers with whom the user interacts and the nature of the interaction(e.g., determining whether the other game players are “friends”,“relatives”, “colleagues”, “strangers”, etc. and how the user interactedwith the other game players—e.g., showed an interest in interacting withthem, avoided them, etc.); and (3) activity a—categorize the type ofactivities performed by the user in the game.

In one embodiment, the profiling module 203 aggregates the results ofthe classification of the features as described above to identify“sensitive” locations (e.g., locations of interest or locationsotherwise associated with the user). By way of example, theidentification of the sensitive locations can include identifying thetype of establishment at the location based on the user contacts withwhom the user interacts/avoids at that location, as well as the type ofactivities performed at that location. In one embodiment, the profilingmodule 203 can use the amount of time the user spends at a location as aparameter to determine the sensitiveness of the location, e.g., alllocations in the game where the user spends time greater than athreshold t can be classified as “sensitive”. The profiling module 203can then transmit the sensitive locations to the other modules of theprivacy platform 109 for further processing.

In one embodiment, the profiling module 203 can interact with theheuristics module 205 to further refine the profiling process. By way ofexample, the heuristics module 205 can improve the accuracy of theprofiling process of the profiling module 203 by employing one or moreheuristics as additional inputs to the feature extraction/classificationprocess. These heuristics include, but are not limited to: (a)iterative/incremental classification, (b) semantic classification, (c)generalization, and (d) integration.

By way of example, to perform interactive/incremental classification,the heuristics module 205 can repeat the classification process as newin-game behavior data about a user becomes available. In one embodiment,this incremental classification can be refined to focus on user actionsat specific locations (e.g., those identified as sensitive locations),with specific contacts, while performing specific activities, etc.

For semantic classification, the heuristics module 205 can use, forinstance, externally available information to better characterize auser's sensitivity to specific locations. For example, the heuristicsmodule 205 can use externally available information, e.g., with respectthe type of establishments located at a specific location. In oneembodiment, the heuristics module 205 can query for the externallyavailable information from one or more network sources including, butnot limited to, the content providers 117, the services platform 115,and/or the services 115. The semantic classification can also be appliedto other extracted features such as contacts (e.g., to query for moreinformation related to specific contacts to determine the sensitivity ofa contact to the user), activities (e.g., to query for informationrelated to the sensitivity of an activity to the user), and the like.

For generalization, the heuristics module 205 can generalize in-gamebehavior exhibited by a user at a specific location to other or alllocations of the same or similar type. For instance, a user can beexpected to exhibit similar behavior at other or all publicparks/places, even though the user's behavior at only one specific parkmight have been captured in the game. In one embodiment, the heuristicsmodule 205 can also generalize other extracted features. For example,in-game behavior with respect to one type of contact or type of activitycan be generalized to other similar types of contacts and/or activities.

In one embodiment, the heuristics module 205 can use integration withother types of feedback mechanisms or data analytics to improve theaccuracy of classification and feature extraction performed by theprofiling module 203. For example, the heuristics module 205 caninterface with or otherwise employ the results of an emotional staterecognition system to determine a user's emotional response to specificlocations, contacts, activities, etc. within a location-based game. Theemotional response data can then be used to improve the accuracy of the“sensitive” locations (or, e.g., sensitive contacts, sensitiveactivities, etc.) detection process.

Based on the classification results generated by the profiling module203 and/or the heuristics module 205, the policy module 207 then definesprivacy policies applicable to real-world locations that correlate tothe in-game locations indicated in a user's in-game behavior data. Inone embodiment, the policy module 207 may define policies only for thoselocations identified as “sensitive” locations as described above.Alternatively, the policy module 207 can define policies for allidentified locations or a subset of the sensitive locations.

In one embodiment, the policy module 207 generates policies that areeither permissive or restrictive. In addition, in one embodiment, thepolicy module 207 can use policy templates in combination with thefeatures extracted by the profiling module 203 to generate policy.However, it is contemplated that the policy module 207 can use any meansfor generating policies, and that the template-based approach describedin the various embodiments are intended by way of illustration and notlimitation.

More specifically, in one embodiment, the classification results areused to define privacy policies to regulate the user interaction Iactivities at identified locations in real-life (e.g., real-life orreal-world locations identified as “sensitive” to a user). In oneembodiment, the privacy policies can be either “permissive” or“restrictive.” For example, permissive policies allow specific actions,functions, etc. to occur at associated locations, while restrictivepolicies restrict or prevent specific actions, functions, etc. atassociated locations.

Example of policy templates used to generate permissive policies andrestrictive policies are as follows:

-   -   a. Permissive policies: At sensitive location(s) <l₁, l₂, . .        . >, allow        -   i. user U's device d to interact with devices <d₁, d₂, . .            . > of users/contacts <c₁, c₂, . . . > respectively.        -   ii. apps related to activities <a₁, a₂, . . . > to            execute—allowing them to            -   1. access sensors <s₁, s₂, . . . > of device d required                to perform activities <a₁, a₂, . . . >, or            -   2. share app data related to activities <a₁, a₂, . . . >                with users/contacts <c₁, c₂, . . . >.    -   b. Restrictive policies: At sensitive location(s) <l₁, l₂, . .        . >, restrict/prevent        -   i. user U's device d from interacting with devices <d₁, d₂,            . . . > of users/contacts <c₁, c₂, . . . > respectively.        -   ii. apps related to activities <a₁, a₂, . . . > from            executing—preventing them from            -   1. accessing sensors <s₁, s₂, . . . > of device d                related to activities <a₁, a₂, . . . >, or            -   2. sharing app data related to activities <a₁, a₂, . .                . > with users/contacts <c₁, c₂, . . . >.

Accordingly, in one embodiment, the policies generation process consistsof mapping the locations l, contacts c, and/or activities a (e.g., asextracted, profiled, classified, etc. by the profiling module 203) totheir placeholders in the policy templates outlined above.

The above presented modules and components of the privacy platform 109can be implemented in hardware, firmware, software, or a combinationthereof. Though depicted as a separate entity in FIG. 1, it iscontemplated that the privacy platform 109 may be implemented for directoperation by respective UE 101. As such, the privacy platform 109 maygenerate direct signal inputs by way of the operating system of the UE101 for interacting with the game applications 103 and otherapplications or services executed on the UE 101. In another embodiment,one or more of the modules 201-207 may be implemented for operation byrespective UEs 101, as a privacy platform 109, or combination thereof.Still further, the privacy platform 109 may be integrated for directoperation with services 115, such as in the form of a widget or applet,in accordance with an information and/or subscriber sharing arrangement.The various embodiments presented herein contemplate any and allarrangements and models.

FIG. 3 is a flowchart of a process for providing privacy policygeneration based on in-game behavior data, according to one embodiment.In one embodiment, the privacy platform 109 performs the process 300 andis implemented in, for instance, a chip set including a processor and amemory as shown in FIG. 8. In addition or alternatively, the privacyplatform module 110 may perform all or a portion of the process 300, andmay also be implemented in the chip set including the processor and thememory as shown in FIG. 8.

In step 301, the privacy platform 109 determines in-game behavior dataassociated with at least one user while the at least one user is playingat least one location-based game. In one embodiment, the in-gamebehavior includes interactions, events, contextual information, and/orany other information generated by a location-based game 103 and/or theUE 101 on which the game 103 is executing. For example, in addition tothe interaction tracking data available in game, the privacy platform109 may also access contemporaneous sensor data for the sensors 105 ofthe device that may provide contextual information (e.g., movement,time, location, activity, other sensed nearby devices, contactinformation, etc.) collected during gameplay. In one embodiment, thelocation-based game 103 may provide application programming interfaces(APIs) to provide access to in-game data. In other examples, the game103 may store in-game behavior data in a database (e.g., database 111)or cloud-based storage that is accessible by the privacy platform 103.

In step 303, the privacy platform 109 causes, at least in part, amapping of the in-game behavior data to one or more game locationswithin the at least one location-based game. In one embodiment, theprivacy platform 109 performs the mapping by classifying the dataaccording to location. For example, the privacy platform 109 may extractlocation information stored in the in-game behavior data or consultcontemporaneous location sensor data to determine an appropriatelocation (e.g., where the location-based game 103 directs a user to anactual physical location as part of gameplay). In this way, the privacyplatform 109 can appropriately identify what portion of the in-gamebehavior was performed or collected with a particular location.

In step 305, the privacy platform 109 causes, at least in part, acorrelation of the one or more game locations to one or more real-worldlocations. In one embodiment, to translate in-game behavior to acorresponding real-world location, the privacy platform 109 candetermine what real-world locations correspond to the game locationsidentified in previous steps. For example, in location-based games 103where users are directed to the actual physical locations, the in-gamelocation has a direct correspondence to a real-world location.

In another embodiment, if the game world is based on a fictional orpartly fictional map or geographical environment, the privacy platform109 can identify features of in-game locations and match them againstreal-world locations that most closely match those features to find acorresponding real-world location. For example, if the user visits afast food restaurant in in a game 103, where the in-game fast foodrestaurant is located within 1 mile of the user's in-game home, theprivacy platform 109 can find a real-world fast food restaurantequivalent to the in-game restaurant at approximately the same distancefrom the user's actual home, and designate that real-world fast foodrestaurant as the equivalent.

In one embodiment, to make the correlation between in-game locations andreal-world locations, the privacy platform can use various heuristicsincluding, e.g., semantic classification and generalization aspreviously discussed. In other words, the correlation of the one or moregame locations to the one or more real-world locations is based, atleast in part, on a generalization of the game locations, the real-worldlocations, or a combination thereof. Generalization, for instance,enables the privacy platform 109 to make real-world locationcorrelations based on determining the type or category of the in-gamelocation (e.g., an example of this generalization is discussed in thepreceding paragraph). In another embodiment, the privacy platform 109can employ semantic classification to discover additional semanticinformation about an in-game location or establishments at the in-gamelocation to facilitate finding a corresponding or correlated real-worldlocation.

In step 307, the privacy platform 109 causes, at least in part, agenerating of one or more privacy policies for the one or morereal-world locations based, at least in part, on the in-game behaviordata mapped to the correlated one or more game locations. As previouslydiscussed, the process for generating privacy policies include profilingthe in-game behavior data for a given location to extract or classifyany number of features (e.g., location, contact, time, activity, etc.).Based on the nature of the interaction data present in the in-gamebehavior data (e.g., engaging in certain activities at the location,avoiding or preferring certain contacts at certain locations, etc.), theprivacy platform 109 applies the extracted features to policy template(e.g., containing placeholders for the features) to generate privacypolicies.

In one embodiment, the one or more privacy policies include one or morepermissive policies, one or more restrictive policies, or a combinationthereof; wherein the one or more permissive policies allow one or morefunctions of at least one device associated with the at least one user;and wherein the one or more restrictive policies restrict the one ormore functions of the at least one device. By way of example, the one ormore functions of the at least one device include, at least in part, oneor more application functions, one or more sensor functions, one or moredata sharing functions, or a combination thereof.

FIG. 4 is a flowchart of a process for classifying in-game behavior togenerate privacy policies, according to one embodiment. In oneembodiment, the privacy platform 109 performs the process 400 and isimplemented in, for instance, a chip set including a processor and amemory as shown in FIG. 8. In addition or alternatively, the privacyplatform module 110 may perform all or a portion of the process 400, andmay also be implemented in the chip set including the processor and thememory as shown in FIG. 8. The process 400 presents optional steps forperforming a feature extraction or data mining of the in-game behaviordata.

In step 401, the privacy platform 109 causes, at least in part, aclassification of the in-game behavior information to determine the oneor more game locations, one or more game contacts, one or moreinteractions with the one or more game contacts, one or more gameactivities, or a combination thereof. It is contemplated that gamelocations, game contact, interactions, and/or game activities areprovided as examples of the features that can be extracted from in-gamebehavior data, and are not provided as illustrated. Accordingly, theapproaches of the various embodiments described herein are applicable toany feature that can be extracted, profiled, or mined from in-gamebehavior and/or related data (e.g., contextual data collected on adevice concurrently during gameplay).

As previously discussed, the privacy platform 109 can employ any numberof heuristics to improve or otherwise facilitate the classification ofthe in-game behavior data. For example, in one embodiment, theclassification is performed incrementally as the in-game behavior databecomes available. In one embodiment, the classification is performedusing a semantic classification. In yet another embodiment, theclassification integrates data available from other complementarysystems (e.g., emotion recognition systems).

As an optional embodiment, the privacy platform 109 can also applyheuristics to features other than location (e.g., contacts, activities,etc.) to improve location-based privacy policies. For example, in step403, the privacy platform 109 causes, at least in part, a correlation ofthe one or more game contacts to one or more real-world contacts. Aswith locations, if in-game contacts do not directly correlate toreal-world contacts, the privacy platform 109 can extract features ofin-game contacts (e.g., category or type such as friends, relatives,colleagues, strangers, etc.) and match them against real-world contactsof the user. For example, a user's behavior with respect to family in agame environment can be translated to expected behavior to family in areal-world environment. In one embodiment, the correlation of the one ormore game contacts to the one or more real-world contacts is based, atleast in part, on similar heuristics applied to location (e.g., semanticclassification, generalization, integration, etc.).

In step 405, the privacy platform 109 generates privacy policies furtherbased on the classification and/or the correlations to real-worldcontacts.

FIG. 5 is a flowchart of a process for determining sensitivity statusinformation for locations based on in-game behavior data, according toone embodiment. In one embodiment, the privacy platform 109 performs theprocess 500 and is implemented in, for instance, a chip set including aprocessor and a memory as shown in FIG. 8. In addition or alternatively,the privacy platform module 110 may perform all or a portion of theprocess 400, and may also be implemented in the chip set including theprocessor and the memory as shown in FIG. 8. The process 500 enables theprivacy platform 109 to limit the number of locations extracted fromin-game behavior data for which privacy policies are needed. Forexample, the process 500 enables the privacy platform 109 to determinewhich locations are most sensitive to a user, and then generate privacypolicies only for those sensitive locations or a subset of the sensitivelocations.

In step 501, the privacy platform 109 processes and/or facilitates aprocessing of the in-game behavior data to determine sensitivity statusinformation for the one or more game locations, the one or morereal-world locations, or a combination thereof. In one embodiment, theprivacy platform 109 determines sensitive locations based on a temporalparameter. For example, the privacy platform 109 determines time spentat the one or more game locations based, at least in part, on thein-game behavior data. In one embodiment, the sensitivity statusinformation is determined based, on the time spent. For example, if thetime spent at a particular location is greater than a threshold value,then the privacy platform 109 designates the location as sensitive. Aspreviously noted, time spent is only one example of a criterion fordesignating a location as sensitive. Other examples include, but are notlimited to, number of visits, recommendation from others, searchhistories, etc.

In step 503, the privacy platform 109 generates privacy policies basedon in-game behavior for the locations designated as sensitive based ontheir associated sensitivity status information.

FIGS. 6A-6C are user interface diagrams depicting a process forproviding privacy policy generation based on in-game behavior data,according to various example embodiments. As shown in FIG. 6A,illustration 601 depicts a scenario in which a user is playing amulti-player location-based augmented reality game that provides foraugmented reality-based game play elements that are overlaid on actualphysical location in the environment. In this example, the user isplaying the game in a park and can see the various augmented realityelements within her local environment.

On an initiation of the location-based game, the privacy platform 109and/or the privacy platform module 110 can present a notification 603 onthe user's device 605 to let the user know that the privacy platform 109has detected that a location-based game in in progress. The notification603 also informs the user that the user's in-game behavior data is beingcollected to facilitate privacy policy generation. The notification 603also provides options 607 for the user to agree to the in-game behaviormonitoring. If the user agrees by selecting the YES button of theoptions 607, the privacy platform 109 and/or the privacy platform module110 begins monitoring the user's in-game behavior to initiate theprivacy policy generation process.

Once sufficient data is collected, the privacy platform 109 and/or theprivacy platform module 110 processes the in-game behavior data todetermine locations that are sensitive to the user. In this example, theuser has spent a greater than then threshold amount of time at the ParkA, Restaurant A, and Home. As shown in FIG. 6B, the privacy platform 109and/or the privacy platform module 110 presents a notification 611 onthe user's device 605 to indicate that sensitive locations have beenfound and lists the sensitive locations for the user. In one embodiment,the privacy platform 109 and/or the privacy platform module 110 can alsopresent an option 613 for the user to confirm or edit the sensitivelocations.

Once the user confirms the locations, the privacy platform 109 and/orthe privacy platform module 110 processes the in-game behavior data(e.g., by extracting locations, contacts, activities, interactions,etc.) associated with those locations to automatically generatelocation-based privacy policies for users at that location. As shown inFIG. 6C, the privacy platform 109 and/or the privacy platform module 110can present a notification 621 that privacy policies have been generatedfor the sensitive locations confirmed by the user. In this case, Park Ais associated with a Policy A, Restaurant A is associated with a PolicyB, and Home is associated with a Policy C. By way of example, thepolicies are specifically tailored or customized for each location basedon the user's in-game behavior. The privacy platform 109 and/or theprivacy platform module 110 also presents the user with an option 623 toconfirm or edit the policies.

Accordingly, by automatically generating the privacy policies for theuser based on the user's in-game behavior data, the privacy platform 109and/or the privacy platform module 110 advantageously avoids the user'shaving to manually specify a policy for each location of potentialinterest. By reducing the burden, the privacy platform 109 and/or theprivacy platform module 110 also advantageously enables the user toimplement customized policies which enable or restrict device functionsas appropriate without resorting to an “All-or-Nothing” approach.

The processes described herein for providing privacy policy generationbased on in-game behavior data may be advantageously implemented viasoftware, hardware, firmware or a combination of software and/orfirmware and/or hardware. For example, the processes described herein,may be advantageously implemented via processor(s), Digital SignalProcessing (DSP) chip, an Application Specific Integrated Circuit(ASIC), Field Programmable Gate Arrays (FPGAs), etc. Such exemplaryhardware for performing the described functions is detailed below.

FIG. 7 illustrates a computer system 700 upon which an embodiment of theinvention may be implemented. Although computer system 700 is depictedwith respect to a particular device or equipment, it is contemplatedthat other devices or equipment (e.g., network elements, servers, etc.)within FIG. 7 can deploy the illustrated hardware and components ofsystem 700. Computer system 700 is programmed (e.g., via computerprogram code or instructions) to provide privacy policy generation basedon in-game behavior data as described herein and includes acommunication mechanism such as a bus 710 for passing informationbetween other internal and external components of the computer system700. Information (also called data) is represented as a physicalexpression of a measurable phenomenon, typically electric voltages, butincluding, in other embodiments, such phenomena as magnetic,electromagnetic, pressure, chemical, biological, molecular, atomic,sub-atomic and quantum interactions. For example, north and southmagnetic fields, or a zero and non-zero electric voltage, represent twostates (0, 1) of a binary digit (bit). Other phenomena can representdigits of a higher base. A superposition of multiple simultaneousquantum states before measurement represents a quantum bit (qubit). Asequence of one or more digits constitutes digital data that is used torepresent a number or code for a character. In some embodiments,information called analog data is represented by a near continuum ofmeasurable values within a particular range. Computer system 700, or aportion thereof, constitutes a means for performing one or more steps ofproviding privacy policy generation based on in-game behavior data.

A bus 710 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus710. One or more processors 702 for processing information are coupledwith the bus 710.

A processor (or multiple processors) 702 performs a set of operations oninformation as specified by computer program code related to providingprivacy policy generation based on in-game behavior data. The computerprogram code is a set of instructions or statements providinginstructions for the operation of the processor and/or the computersystem to perform specified functions. The code, for example, may bewritten in a computer programming language that is compiled into anative instruction set of the processor. The code may also be writtendirectly using the native instruction set (e.g., machine language). Theset of operations include bringing information in from the bus 710 andplacing information on the bus 710. The set of operations also typicallyinclude comparing two or more units of information, shifting positionsof units of information, and combining two or more units of information,such as by addition or multiplication or logical operations like OR,exclusive OR (XOR), and AND. Each operation of the set of operationsthat can be performed by the processor is represented to the processorby information called instructions, such as an operation code of one ormore digits. A sequence of operations to be executed by the processor702, such as a sequence of operation codes, constitute processorinstructions, also called computer system instructions or, simply,computer instructions. Processors may be implemented as mechanical,electrical, magnetic, optical, chemical or quantum components, amongothers, alone or in combination.

Computer system 700 also includes a memory 704 coupled to bus 710. Thememory 704, such as a random access memory (RAM) or any other dynamicstorage device, stores information including processor instructions forproviding privacy policy generation based on in-game behavior data.Dynamic memory allows information stored therein to be changed by thecomputer system 700. RAM allows a unit of information stored at alocation called a memory address to be stored and retrievedindependently of information at neighboring addresses. The memory 704 isalso used by the processor 702 to store temporary values duringexecution of processor instructions. The computer system 700 alsoincludes a read only memory (ROM) 706 or any other static storage devicecoupled to the bus 710 for storing static information, includinginstructions, that is not changed by the computer system 700. Somememory is composed of volatile storage that loses the information storedthereon when power is lost. Also coupled to bus 710 is a non-volatile(persistent) storage device 708, such as a magnetic disk, optical diskor flash card, for storing information, including instructions, thatpersists even when the computer system 700 is turned off or otherwiseloses power.

Information, including instructions for providing privacy policygeneration based on in-game behavior data, is provided to the bus 710for use by the processor from an external input device 712, such as akeyboard containing alphanumeric keys operated by a human user, or asensor. A sensor detects conditions in its vicinity and transforms thosedetections into physical expression compatible with the measurablephenomenon used to represent information in computer system 700. Otherexternal devices coupled to bus 710, used primarily for interacting withhumans, include a display device 714, such as a cathode ray tube (CRT),a liquid crystal display (LCD), a light emitting diode (LED) display, anorganic LED (OLED) display, a plasma screen, or a printer for presentingtext or images, and a pointing device 716, such as a mouse, a trackball,cursor direction keys, or a motion sensor, for controlling a position ofa small cursor image presented on the display 714 and issuing commandsassociated with graphical elements presented on the display 714. In someembodiments, for example, in embodiments in which the computer system700 performs all functions automatically without human input, one ormore of external input device 712, display device 714 and pointingdevice 716 is omitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 720, is coupled to bus710. The special purpose hardware is configured to perform operationsnot performed by processor 702 quickly enough for special purposes.Examples of ASICs include graphics accelerator cards for generatingimages for display 714, cryptographic boards for encrypting anddecrypting messages sent over a network, speech recognition, andinterfaces to special external devices, such as robotic arms and medicalscanning equipment that repeatedly perform some complex sequence ofoperations that are more efficiently implemented in hardware.

Computer system 700 also includes one or more instances of acommunications interface 770 coupled to bus 710. Communication interface770 provides a one-way or two-way communication coupling to a variety ofexternal devices that operate with their own processors, such asprinters, scanners and external disks. In general the coupling is with anetwork link 778 that is connected to a local network 780 to which avariety of external devices with their own processors are connected. Forexample, communication interface 770 may be a parallel port or a serialport or a universal serial bus (USB) port on a personal computer. Insome embodiments, communications interface 770 is an integrated servicesdigital network (ISDN) card or a digital subscriber line (DSL) card or atelephone modem that provides an information communication connection toa corresponding type of telephone line. In some embodiments, acommunication interface 770 is a cable modem that converts signals onbus 710 into signals for a communication connection over a coaxial cableor into optical signals for a communication connection over a fiberoptic cable. As another example, communications interface 770 may be alocal area network (LAN) card to provide a data communication connectionto a compatible LAN, such as Ethernet. Wireless links may also beimplemented. For wireless links, the communications interface 770 sendsor receives or both sends and receives electrical, acoustic orelectromagnetic signals, including infrared and optical signals, thatcarry information streams, such as digital data. For example, inwireless handheld devices, such as mobile telephones like cell phones,the communications interface 770 includes a radio band electromagnetictransmitter and receiver called a radio transceiver. In certainembodiments, the communications interface 770 enables connection to thecommunication network 107 for providing privacy policy generation basedon in-game behavior data to the UE 101.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing information to processor 702, includinginstructions for execution. Such a medium may take many forms,including, but not limited to computer-readable storage medium (e.g.,non-volatile media, volatile media), and transmission media.Non-transitory media, such as non-volatile media, include, for example,optical or magnetic disks, such as storage device 708. Volatile mediainclude, for example, dynamic memory 704. Transmission media include,for example, twisted pair cables, coaxial cables, copper wire, fiberoptic cables, and carrier waves that travel through space without wiresor cables, such as acoustic waves and electromagnetic waves, includingradio, optical and infrared waves. Signals include man-made transientvariations in amplitude, frequency, phase, polarization or otherphysical properties transmitted through the transmission media. Commonforms of computer-readable media include, for example, a floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape,optical mark sheets, any other physical medium with patterns of holes orother optically recognizable indicia, a RAM, a PROM, an EPROM, aFLASH-EPROM, an EEPROM, a flash memory, any other memory chip orcartridge, a carrier wave, or any other medium from which a computer canread. The term computer-readable storage medium is used herein to referto any computer-readable medium except transmission media.

Logic encoded in one or more tangible media includes one or both ofprocessor instructions on a computer-readable storage media and specialpurpose hardware, such as ASIC 720.

Network link 778 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 778 mayprovide a connection through local network 780 to a host computer 782 orto equipment 784 operated by an Internet Service Provider (ISP). ISPequipment 784 in turn provides data communication services through thepublic, world-wide packet-switching communication network of networksnow commonly referred to as the Internet 790.

A computer called a server host 792 connected to the Internet hosts aprocess that provides a service in response to information received overthe Internet. For example, server host 792 hosts a process that providesinformation representing video data for presentation at display 714. Itis contemplated that the components of system 700 can be deployed invarious configurations within other computer systems, e.g., host 782 andserver 792.

At least some embodiments of the invention are related to the use ofcomputer system 700 for implementing some or all of the techniquesdescribed herein. According to one embodiment of the invention, thosetechniques are performed by computer system 700 in response to processor702 executing one or more sequences of one or more processorinstructions contained in memory 704. Such instructions, also calledcomputer instructions, software and program code, may be read intomemory 704 from another computer-readable medium such as storage device708 or network link 778. Execution of the sequences of instructionscontained in memory 704 causes processor 702 to perform one or more ofthe method steps described herein. In alternative embodiments, hardware,such as ASIC 720, may be used in place of or in combination withsoftware to implement the invention. Thus, embodiments of the inventionare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link 778 and other networks throughcommunications interface 770, carry information to and from computersystem 700. Computer system 700 can send and receive information,including program code, through the networks 780, 790 among others,through network link 778 and communications interface 770. In an exampleusing the Internet 790, a server host 792 transmits program code for aparticular application, requested by a message sent from computer 700,through Internet 790, ISP equipment 784, local network 780 andcommunications interface 770. The received code may be executed byprocessor 702 as it is received, or may be stored in memory 704 or instorage device 708 or any other non-volatile storage for laterexecution, or both. In this manner, computer system 700 may obtainapplication program code in the form of signals on a carrier wave.

Various forms of computer readable media may be involved in carrying oneor more sequence of instructions or data or both to processor 702 forexecution. For example, instructions and data may initially be carriedon a magnetic disk of a remote computer such as host 782. The remotecomputer loads the instructions and data into its dynamic memory andsends the instructions and data over a telephone line using a modem. Amodem local to the computer system 700 receives the instructions anddata on a telephone line and uses an infra-red transmitter to convertthe instructions and data to a signal on an infra-red carrier waveserving as the network link 778. An infrared detector serving ascommunications interface 770 receives the instructions and data carriedin the infrared signal and places information representing theinstructions and data onto bus 710. Bus 710 carries the information tomemory 704 from which processor 702 retrieves and executes theinstructions using some of the data sent with the instructions. Theinstructions and data received in memory 704 may optionally be stored onstorage device 708, either before or after execution by the processor702.

FIG. 8 illustrates a chip set or chip 800 upon which an embodiment ofthe invention may be implemented. Chip set 800 is programmed to provideprivacy policy generation based on in-game behavior data as describedherein and includes, for instance, the processor and memory componentsdescribed with respect to FIG. 7 incorporated in one or more physicalpackages (e.g., chips). By way of example, a physical package includesan arrangement of one or more materials, components, and/or wires on astructural assembly (e.g., a baseboard) to provide one or morecharacteristics such as physical strength, conservation of size, and/orlimitation of electrical interaction. It is contemplated that in certainembodiments the chip set 800 can be implemented in a single chip. It isfurther contemplated that in certain embodiments the chip set or chip800 can be implemented as a single “system on a chip.” It is furthercontemplated that in certain embodiments a separate ASIC would not beused, for example, and that all relevant functions as disclosed hereinwould be performed by a processor or processors. Chip set or chip 800,or a portion thereof, constitutes a means for performing one or moresteps of providing user interface navigation information associated withthe availability of functions. Chip set or chip 800, or a portionthereof, constitutes a means for performing one or more steps ofproviding privacy policy generation based on in-game behavior data.

In one embodiment, the chip set or chip 800 includes a communicationmechanism such as a bus 801 for passing information among the componentsof the chip set 800. A processor 803 has connectivity to the bus 801 toexecute instructions and process information stored in, for example, amemory 805. The processor 803 may include one or more processing coreswith each core configured to perform independently. A multi-coreprocessor enables multiprocessing within a single physical package.Examples of a multi-core processor include two, four, eight, or greaternumbers of processing cores. Alternatively or in addition, the processor803 may include one or more microprocessors configured in tandem via thebus 801 to enable independent execution of instructions, pipelining, andmultithreading. The processor 803 may also be accompanied with one ormore specialized components to perform certain processing functions andtasks such as one or more digital signal processors (DSP) 807, or one ormore application-specific integrated circuits (ASIC) 809. A DSP 807typically is configured to process real-world signals (e.g., sound) inreal time independently of the processor 803. Similarly, an ASIC 809 canbe configured to performed specialized functions not easily performed bya more general purpose processor. Other specialized components to aid inperforming the inventive functions described herein may include one ormore field programmable gate arrays (FPGA) (not shown), one or morecontrollers (not shown), or one or more other special-purpose computerchips.

In one embodiment, the chip set or chip 800 includes merely one or moreprocessors and some software and/or firmware supporting and/or relatingto and/or for the one or more processors.

The processor 803 and accompanying components have connectivity to thememory 805 via the bus 801. The memory 805 includes both dynamic memory(e.g., RAM, magnetic disk, writable optical disk, etc.) and staticmemory (e.g., ROM, CD-ROM, etc.) for storing executable instructionsthat when executed perform the inventive steps described herein toprovide privacy policy generation based on in-game behavior data. Thememory 805 also stores the data associated with or generated by theexecution of the inventive steps.

FIG. 9 is a diagram of exemplary components of a mobile terminal (e.g.,handset) for communications, which is capable of operating in the systemof FIG. 1, according to one embodiment. In some embodiments, mobileterminal 901, or a portion thereof, constitutes a means for performingone or more steps of providing privacy policy generation based onin-game behavior data. Generally, a radio receiver is often defined interms of front-end and back-end characteristics. The front-end of thereceiver encompasses all of the Radio Frequency (RF) circuitry whereasthe back-end encompasses all of the base-band processing circuitry. Asused in this application, the term “circuitry” refers to both: (1)hardware-only implementations (such as implementations in only analogand/or digital circuitry), and (2) to combinations of circuitry andsoftware (and/or firmware) (such as, if applicable to the particularcontext, to a combination of processor(s), including digital signalprocessor(s), software, and memory(ies) that work together to cause anapparatus, such as a mobile phone or server, to perform variousfunctions). This definition of “circuitry” applies to all uses of thisterm in this application, including in any claims. As a further example,as used in this application and if applicable to the particular context,the term “circuitry” would also cover an implementation of merely aprocessor (or multiple processors) and its (or their) accompanyingsoftware/or firmware. The term “circuitry” would also cover ifapplicable to the particular context, for example, a baseband integratedcircuit or applications processor integrated circuit in a mobile phoneor a similar integrated circuit in a cellular network device or othernetwork devices.

Pertinent internal components of the telephone include a Main ControlUnit (MCU) 903, a Digital Signal Processor (DSP) 905, and areceiver/transmitter unit including a microphone gain control unit and aspeaker gain control unit. A main display unit 907 provides a display tothe user in support of various applications and mobile terminalfunctions that perform or support the steps of providing privacy policygeneration based on in-game behavior data. The display 907 includesdisplay circuitry configured to display at least a portion of a userinterface of the mobile terminal (e.g., mobile telephone). Additionally,the display 907 and display circuitry are configured to facilitate usercontrol of at least some functions of the mobile terminal. An audiofunction circuitry 909 includes a microphone 911 and microphoneamplifier that amplifies the speech signal output from the microphone911. The amplified speech signal output from the microphone 911 is fedto a coder/decoder (CODEC) 913.

A radio section 915 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 917. The power amplifier (PA) 919 andthe transmitter/modulation circuitry are operationally responsive to theMCU 903, with an output from the PA 919 coupled to the duplexer 921 orcirculator or antenna switch, as known in the art. The PA 919 alsocouples to a battery interface and power control unit 920.

In use, a user of mobile terminal 901 speaks into the microphone 911 andhis or her voice along with any detected background noise is convertedinto an analog voltage. The analog voltage is then converted into adigital signal through the Analog to Digital Converter (ADC) 923. Thecontrol unit 903 routes the digital signal into the DSP 905 forprocessing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., microwave access (WiMAX), LongTerm Evolution (LTE) networks, code division multiple access (CDMA),wideband code division multiple access (WCDMA), wireless fidelity(WiFi), satellite, and the like, or any combination thereof.

The encoded signals are then routed to an equalizer 925 for compensationof any frequency-dependent impairments that occur during transmissionthough the air such as phase and amplitude distortion. After equalizingthe bit stream, the modulator 927 combines the signal with a RF signalgenerated in the RF interface 929. The modulator 927 generates a sinewave by way of frequency or phase modulation. In order to prepare thesignal for transmission, an up-converter 931 combines the sine waveoutput from the modulator 927 with another sine wave generated by asynthesizer 933 to achieve the desired frequency of transmission. Thesignal is then sent through a PA 919 to increase the signal to anappropriate power level. In practical systems, the PA 919 acts as avariable gain amplifier whose gain is controlled by the DSP 905 frominformation received from a network base station. The signal is thenfiltered within the duplexer 921 and optionally sent to an antennacoupler 935 to match impedances to provide maximum power transfer.Finally, the signal is transmitted via antenna 917 to a local basestation. An automatic gain control (AGC) can be supplied to control thegain of the final stages of the receiver. The signals may be forwardedfrom there to a remote telephone which may be another cellulartelephone, any other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile terminal 901 are received viaantenna 917 and immediately amplified by a low noise amplifier (LNA)937. A down-converter 939 lowers the carrier frequency while thedemodulator 941 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 925 and is processed by theDSP 905. A Digital to Analog Converter (DAC) 943 converts the signal andthe resulting output is transmitted to the user through the speaker 945,all under control of a Main Control Unit (MCU) 903 which can beimplemented as a Central Processing Unit (CPU) (not shown).

The MCU 903 receives various signals including input signals from thekeyboard 947. The keyboard 947 and/or the MCU 903 in combination withother user input components (e.g., the microphone 911) comprise a userinterface circuitry for managing user input. The MCU 903 runs a userinterface software to facilitate user control of at least some functionsof the mobile terminal 901 to provide privacy policy generation based onin-game behavior data. The MCU 903 also delivers a display command and aswitch command to the display 907 and to the speech output switchingcontroller, respectively. Further, the MCU 903 exchanges informationwith the DSP 905 and can access an optionally incorporated SIM card 949and a memory 951. In addition, the MCU 903 executes various controlfunctions required of the terminal. The DSP 905 may, depending upon theimplementation, perform any of a variety of conventional digitalprocessing functions on the voice signals. Additionally, DSP 905determines the background noise level of the local environment from thesignals detected by microphone 911 and sets the gain of microphone 911to a level selected to compensate for the natural tendency of the userof the mobile terminal 901.

The CODEC 913 includes the ADC 923 and DAC 943. The memory 951 storesvarious data including call incoming tone data and is capable of storingother data including music data received via, e.g., the global Internet.The software module could reside in RAM memory, flash memory, registers,or any other form of writable storage medium known in the art. Thememory device 951 may be, but not limited to, a single memory, CD, DVD,ROM, RAM, EEPROM, optical storage, magnetic disk storage, flash memorystorage, or any other non-volatile storage medium capable of storingdigital data.

An optionally incorporated SIM card 949 carries, for instance, importantinformation, such as the cellular phone number, the carrier supplyingservice, subscription details, and security information. The SIM card949 serves primarily to identify the mobile terminal 901 on a radionetwork. The card 949 also contains a memory for storing a personaltelephone number registry, text messages, and user specific mobileterminal settings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

1. A method comprising: determining in-game behavior data associatedwith at least one user while the at least one user is playing at leastone location-based game; causing, at least in part, a mapping of thein-game behavior data to one or more game locations within the at leastone location-based game; causing, at least in part, a correlation of theone or more game locations to one or more real-world locations; andcausing, at least in part, a generating of one or more privacy policiesfor the one or more real-world locations based, at least in part, on thein-game behavior data mapped to the correlated one or more gamelocations.
 2. A method of claim 1, further comprising: causing, at leastin part, a classification of the in-game behavior information todetermine the one or more game locations, one or more game contacts, oneor more interactions with the one or more game contacts, one or moregame activities, or a combination thereof, wherein the one or moreprivacy policies are further based, at least in part, on theclassification.
 3. A method of claim 2, further comprising: causing, atleast in part, a correlation of the one or more game contacts to one ormore real-world contacts, wherein the one or more privacy policies arefurther generated with respect to the one or more real-world contacts.4. A method of claim 2, wherein the classification is performedincrementally as the in-game behavior data becomes available.
 5. Amethod of claim 2, wherein the classification is performed using asemantic classification.
 6. A method of claim 1, further comprising:causing, at least in part, a generalization of the one or more gamelocations, wherein the correlation of the one or more game locations tothe one or more real-world locations is based, at least in part, on thegeneralization.
 7. A method of claim 1, further comprising: processingand/or facilitating a processing of the in-game behavior data todetermine sensitivity status information for the one or more gamelocations, the one or more real-world locations, or a combinationthereof.
 8. A method of claim 7, further comprising: determining timespent at the one or more game locations based, at least in part, on thein-game behavior data, wherein the sensitivity status information isbased, at least in part, on the time spent.
 9. A method of claim 1,wherein the one or more privacy policies include one or more permissivepolicies, one or more restrictive policies, or a combination thereof;wherein the one or more permissive policies allow one or more functionsof at least one device associated with the at least one user; andwherein the one or more restrictive policies restrict the one or morefunctions of the at least one device.
 10. A method of claim 9, whereinthe one or more functions of the at least one device include, at leastin part, one or more application functions, one or more sensorfunctions, one or more data sharing functions, or a combination thereof.11. An apparatus comprising: at least one processor; and at least onememory including computer program code for one or more programs, the atleast one memory and the computer program code configured to, with theat least one processor, cause the apparatus to perform at least thefollowing, determine in-game behavior data associated with at least oneuser while the at least one user is playing at least one location-basedgame; cause, at least in part, a mapping of the in-game behavior data toone or more game locations within the at least one location-based game;cause, at least in part, a correlation of the one or more game locationsto one or more real-world locations; and cause, at least in part, agenerating of one or more privacy policies for the one or morereal-world locations based, at least in part, on the in-game behaviordata mapped to the correlated one or more game locations.
 12. Anapparatus of claim 11, wherein the apparatus is further caused to:cause, at least in part, a classification of the in-game behaviorinformation to determine the one or more game locations, one or moregame contacts, one or more interactions with the one or more gamecontacts, one or more game activities, or a combination thereof, whereinthe one or more privacy policies are further based, at least in part, onthe classification.
 13. An apparatus of claim 12, wherein the apparatusis further caused to: cause, at least in part, a correlation of the oneor more game contacts to one or more real-world contacts, wherein theone or more privacy policies are further generated with respect to theone or more real-world contacts.
 14. An apparatus of claim 12, whereinthe classification is performed incrementally as the in-game behaviordata becomes available, performed using a semantic classification, or acombination thereof.
 15. An apparatus of claim 11, wherein the apparatusis further caused to: cause, at least in part, a generalization of theone or more game locations, wherein the correlation of the one or moregame locations to the one or more real-world locations is based, atleast in part, on the generalization.
 16. An apparatus of claim 11,wherein the apparatus is further caused to: process and/or facilitate aprocessing of the in-game behavior data to determine sensitivity statusinformation for the one or more game locations, the one or morereal-world locations, or a combination thereof.
 17. A computer-readablestorage medium carrying one or more sequences of one or moreinstructions which, when executed by one or more processors, cause anapparatus to perform: determining in-game behavior data associated withat least one user while the at least one user is playing at least onelocation-based game; causing, at least in part, a mapping of the in-gamebehavior data to one or more game locations within the at least onelocation-based game; causing, at least in part, a correlation of the oneor more game locations to one or more real-world locations; and causing,at least in part, a generating of one or more privacy policies for theone or more real-world locations based, at least in part, on the in-gamebehavior data mapped to the correlated one or more game locations.
 18. Acomputer-readable storage medium of claim 17, wherein the apparatus isfurther caused to perform: causing, at least in part, a classificationof the in-game behavior information to determine the one or more gamelocations, one or more game contacts, one or more interactions with theone or more game contacts, one or more game activities, or a combinationthereof, wherein the one or more privacy policies are further based, atleast in part, on the classification.
 19. A computer-readable storagemedium of claim 18, wherein the apparatus is further caused to perform:causing, at least in part, a correlation of the one or more gamecontacts to one or more real-world contacts, wherein the one or moreprivacy policies are further generated with respect to the one or morereal-world contacts.
 20. A computer-readable storage medium of claim 17,wherein the apparatus is further caused to perform: processing and/orfacilitating a processing of the in-game behavior data to determinesensitivity status information for the one or more game locations, theone or more real-world locations, or a combination thereof. 21-48.(canceled)